loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Giuliano Alves da Silva 1 ; Maria Cláudia Ferrari de Castro 2 and Carlos Eduardo Thomaz 1

Affiliations: 1 Centro Universitário da FEI, Brazil ; 2 Centro Universitario da FEI, Brazil

ISBN: 978-989-8111-65-4

Keyword(s): Electromyography, Biceps, Triceps, Linear Transformation, PCA, MLDA, Bhattacharyya Distance.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Pattern recognition of electromyographic signals consists of a hard task due to the high dimensionality of the data and noise presence on the acquired signals. This work intends to study the data set as a multivariate pattern recognition problem by applying linear transformations to reduce the data dimensionality. Five volunteers contributed in a previous experiment that acquired the myoelectrical signals using surface electrodes. Attempts to analyse the groups of acquired data by means of descriptive statistics have shown to be inconclusive. This works shows that the use of multivariate statistical techniques such as Principal Components Analysis (PCA) and Maximum uncertainty Linear Discriminant Analysis (MLDA) to characterize the acquired set of signals through low dimensional scatter plots provides a new understanding of the data spread, making easier its analysis. Considering the arm horizontal movement and the acquired set of data used in this research, a multivariate linear sepa ration between the patterns of interest quantified by the distance of Bhattacharyya suggests that it’s possible not only to characterize the angular joint position, but also to confirm that different movements recruit similar amounts of energy to be executed. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.2.53

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Silva G.; Castro M.; Thomaz C. and (2009). A MULTIVARIATE STATISTICAL ANALYSIS OF MUSCULAR BIOPOTENTIAL FOR HUMAN ARM MOVEMENT CHARACTERIZATION.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 227-232. DOI: 10.5220/0001433802270232

@conference{biosignals09,
author={Giuliano Alves da Silva and Maria Cláudia Ferrari de Castro and Carlos Eduardo Thomaz},
title={A MULTIVARIATE STATISTICAL ANALYSIS OF MUSCULAR BIOPOTENTIAL FOR HUMAN ARM MOVEMENT CHARACTERIZATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={227-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001433802270232},
isbn={978-989-8111-65-4},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - A MULTIVARIATE STATISTICAL ANALYSIS OF MUSCULAR BIOPOTENTIAL FOR HUMAN ARM MOVEMENT CHARACTERIZATION
SN - 978-989-8111-65-4
AU - Silva, G.
AU - Castro, M.
AU - Thomaz, C.
PY - 2009
SP - 227
EP - 232
DO - 10.5220/0001433802270232

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.